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Comparisons

Kimi K2.5 vs Qwen3 235B A22B Instruct 2507 FP8 Throughput

On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $0.50 for Kimi K2.5: 2.5x apart. Output is $0.60 against $2.80 (4.7x). On Allocate both bill at list plus the 7% transaction fee.

Kimi K2.5 Qwen3 235B A22B Instruct 2507 FP8 Throughput
LabTogethercomputerQwen
AccessOpen weightsOpen weights
Context window256K tokens256K tokens
List price, input$0.5 / M tokens$0.2 / M tokens
List price, output$2.8 / M tokens$0.6 / M tokens
Cached inputn/an/a
LicenseNot listedApache 2.0
Fine-tunableYesYes

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $450 a month on Qwen3 235B A22B Instruct 2507 FP8 Throughput and $1,580 on Kimi K2.5 at list: a gap of $1,130, or 3.5x.

Qwen3 235B A22B Instruct 2507 FP8 Throughput$0.20$0.60
Kimi K2.5$0.50$2.80
InputOutput

Choose Kimi K2.5 for

  • Whole-document reasoning
  • Long-context retrieval
  • Open-weight fine-tuning
Kimi K2.5 details

Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for

  • The lower list price ($0.20 in / $0.60 out per M tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Qwen3 235B A22B Instruct 2507 FP8 Throughput details

Common questions

Which is cheaper, Kimi K2.5 or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Qwen3 235B A22B Instruct 2507 FP8 Throughput, on this workload shape. At list prices it is $0.20/$0.60 per million tokens in and out against $0.50/$2.80 for Kimi K2.5. Billed on Allocate: $0.21/$0.64 against $0.54/$3.00, list plus 7%.

Which has the bigger context window?

They match: both read 262,144 tokens (256K) per request.

Can I fine-tune Kimi K2.5 or Qwen3 235B A22B Instruct 2507 FP8 Throughput?

Both publish open weights (Kimi K2.5: Not listed; Qwen3 235B A22B Instruct 2507 FP8 Throughput: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

Qwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3-VL-8B-InstructKimi K2.5 vs Qwen3.6 PlusKimi K2.5 vs Qwen3.5 397B A17bQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.5 397B A17bKimi K2.5 vs Qwen3.7 MaxQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.7 Max

Run the numbers on your workload

LLM price comparisonAgent cost calculatorKimi K2.5Qwen3 235B A22B Instruct 2507 FP8 Throughput

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.

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